Mailman 3 September 2010 - NumPy-Discussion

mean of empty sequence gives nan
by Peter Butterworth 28 Sep '10

28 Sep '10

In numpy 1.5.0, I got the following for mean of an empty sequence (or array): In [21]: mean([]) Warning: invalid value encountered in double_scalars Out[21]: nan Is this behaviour expected ? Also, would it be possible to have more explicit warning messages about the problem being related numpy ? It seems these message cause quite a bit of confusion to users. The following would be more helpful : Warning: invalid value encountered in numpy double_scalars

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str/bytes object from arr.data?
by Francesc Alted 27 Sep '10

27 Sep '10

Hi, Anybody knows a way to get a str object (bytes for Python >= 2.6) out of a buffer object (i.e. the .data attribute of ndarrays) without copying data? I need this for avoid creating a new function that deals with buffer objects (instead of reusing the one for str/byte objects that I already have). So, a matter of laziness :-) Thanks, -- Francesc Alted

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Asking for opinions: Priops
by Friedrich Romstedt 27 Sep '10

27 Sep '10

I just ran across the problem of priorities with ndarrays again and it keeps biting me. I did once ago a workaround to get my ``undarray`` class's methods be called when being the second operand of e.g. <ndarray> + <undarray>. But since I wrote it, always Python crashes on exit with the message: Python-32(68665) malloc: *** error for object 0x239680: incorrect checksum for freed object - object was probably modified after being freed. *** set a breakpoint in malloc_error_break to debug (Of course only if I imported the module. Occasionally I also observed Bus errors, and even segfaults.) I overloaded the numpy ops via numpy.set_numeric_ops() with self-written classes, which are *not* derived from numpy.ufunc, and do not resemble numpy ufuncs completely. So I want to do it properly this time. I therefore started with writing a Letter of Intent, and put it online on http://github.com/friedrichromstedt/priops . Opinions? Friedrich P.S.: I will start coding anyway, but it would be nice. P.P.S.: The package this originates from is also online, under http://github.com/friedrichromstedt/upy, or http://upy.sourceforge.net. I will probably create a small example script demonstrating the crash.

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F2py and Absoft Fortran compiler v11.0 (unable to locate U77.lib) [patch]
by Åsmund Hjulstad 25 Sep '10

25 Sep '10

Hi all, I have a small patch to absoft.py: Compiling my fortran extension using f2py and distutils from Numpy 1.5, with Python 2.7 on Windows, using Absoft v11.0 and MSVC 2008 Pro, first, it complained about not finding the library U77.lib. Removing this from the library list (in absoft.py) gave me this error: error LNK2001: unresolved external symbol __absoft_s_cat This symbol is now exported from amisc.lib, and after adding this to the library list everything works fine. The end result is the following patch: (or just replacing U77 with amisc) --- C:/Python27/Lib/site-packages/numpy/distutils/fcompiler/absoft_patched.py Fri Sep 24 15:10:57 2010 +++ C:/Python27/Lib/site-packages/numpy/distutils/fcompiler/absoft.py Fri Sep 24 15:09:52 2010 @@ -91,7 +91,9 @@ def get_libraries(self): opt = FCompiler.get_libraries(self) - if self.get_version() >= '10.0': + if self.get_version() >= '11.0': + opt.extend(['af90math', 'afio', 'af77math', 'amisc']) + elif self.get_version() >= '10.0': opt.extend(['af90math', 'afio', 'af77math', 'U77']) elif self.get_version() >= '8.0': opt.extend(['f90math','fio','f77math','U77']) BTW, the performance increase compared to gfortran v4.5.0 is really noticeable, perhaps more than 50%. Best regards, Åsmund Hjulstad

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Slicing, sum, etc. reduces rank of array?
by George 25 Sep '10

25 Sep '10

I couldn't find an answer to my newbie question, so I'm posting it here. I have: a=numpy.array([[1,2],[3,4]]) b=numpy.array([[5,6],[7,8]]) Via broadcasting, I know that a*[[5],[7]]=numpy.array([[5,10],[21,28]]) Being a recent convert from MATLAB, I expected the same result from a*b[:,0], assuming b[:,0] would be the column vector [[5],[7]]. Unfortunately, I was wrong. b[:,0] is apparently a 1-rank array of shape (2,). This causes a*b[:,0] to evaluate as a*numpy.array([[5,7]])=numpy.array([[5,14],[15,28]]) instead of a*numpy.array([[5],[7]]) To get the result I desire, the only way I've been able to come up with is a*b[:,0].reshape(2,1) to "coerce" b[:,0] into a column vector. Is there an easier way to do this, without having to do the reshape? I find similar things happen when I use other operations (e.g. "sum") that also seem to reduce the array rank. For example, I would expect numpy.sum(b,1) to also be a "column vector," but it also evaluates to a 1-rank array [11, 15] with shape (2,) Any thoughts, suggestions?

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[PATCH] F2Py on Python 3
by Lisandro Dalcin 24 Sep '10

24 Sep '10

It seems that lib2to3 does not process the main f2py bootstrap script that gets autogenerated by f2py's setup.py. The trivial patch below replaces the print statements with sys.stderr.write() calls. After that change, f2py works just fine in Python 3.2 Index: numpy/f2py/setup.py =================================================================== --- numpy/f2py/setup.py (revision 8716) +++ numpy/f2py/setup.py (working copy) @@ -62,7 +62,7 @@ except ValueError: pass os.environ["NO_SCIPY_IMPORT"]="f2py" if mode=="g3-numpy": - print >> sys.stderr, "G3 f2py support is not implemented, yet." + sys.stderr.write("G3 f2py support is not implemented, yet.\n") sys.exit(1) elif mode=="2e-numeric": from f2py2e import main @@ -72,7 +72,7 @@ elif mode=="2e-numpy": from numpy.f2py import main else: - print >> sys.stderr, "Unknown mode:",`mode` + sys.stderr.write("Unknown mode: '%s'\n" % mode) sys.exit(1) main() '''%(os.path.basename(sys.executable))) -- Lisandro Dalcin --------------- CIMEC (INTEC/CONICET-UNL) Predio CONICET-Santa Fe Colectora RN 168 Km 472, Paraje El Pozo Tel: +54-342-4511594 (ext 1011) Tel/Fax: +54-342-4511169

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constructing an array from memory
by Mathew Yeates 24 Sep '10

24 Sep '10

I'm trying to do something ... unusual. gdb support scripting with Python. From within my python script, I can get the address of a contiguous area of memory that stores a fortran array. I want to creat a NumPy array using "frombuffer". I see that the CPython API supports the creation of a buffer, but, is there an easier, more direct, way? -Mathew

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dtype from |S10 to object in array?
by keekychen.shared 24 Sep '10

24 Sep '10

Dear All, See below code pls, import sicpy import numpy as np x = np.zeros((2,),dtype=('i4,f4,a10')) x[:] = [(1,2.,'Hello'),(2,3.,"World")] y = x['f2'] #array(['Hello', 'World'], dtype='|S10') x['f2'] = y x #array([(1, 2.0, 'Hello'), (2, 3.0, 'World')], dtype=[('f0', '<i4'), ('f1', '<f4'), ('f2', '|S10')]) y = y.astype('object') y array([Hello, World], dtype=object) x['f2'] = y array([(1, 2.0, 'HellWorld'), (2, 3.0, '\x00\x00\x00\x00\x00\x00\x18')], dtype=[('f0', '<i4'), ('f1', '<f4'), ('f2', '|S10')]) ##here comes the problem: the f2 col type has not been changed and the data is not I wanted... ---------------------------------------------------------------------------- here is why I need using this: suppose I have a datasource, csv, sql based db or what ever look like this: 1, 2.0, 'Hello' 2, 3.0, 'World' 3, 2.0, 'other string' I want to read them to a numpy array and process it's columns, it has no problem for processing the float or int type but string. After reading the manual and found the object dtype may store variable string then I want to exact the string col into an new array, try to process it then store back to the numpy "matrix" then store it back to the data source. May I know how I can do that? I do not care performance now. Thanks for any hints Rgs, KC

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Commit mailing list
by Stéfan van der Walt 23 Sep '10

23 Sep '10

Hi all, The commit mailing list is running again. Regards Stéfan

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slicing / indexing question
by Timothy W. Hilton 23 Sep '10

23 Sep '10

Hello, I have an indexing problem which I suspect has a simple solution, but I've not been able to piece together various threads I've read on this list to solve. I have an 80x1200x1200 nd.array of floats this_par. I have a 1200x1200 boolean array idx, and an 80-element float array pars. For each element of idx that is True, I wish to replace the corresponding 80x1x1 slice of this_par with the elements of pars. I've tried lots of variations on the theme of >>>this_par[idx[np.newaxis, ...]] = pars[:, np.newaxis, np.newaxis] but so far, no dice. Any help greatly appreciated! Thanks, Tim -- Timothy W. Hilton PhD Candidate, Department of Meteorology The Pennsylvania State University 503 Walker Building, University Park, PA 16802 hilton(a)meteo.psu.edu

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